diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 0c4e6641444f1..a9f69ee4f6c57 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -127,9 +127,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.DataFrame.to_period SA01" \
         -i "pandas.DataFrame.to_timestamp SA01" \
         -i "pandas.DataFrame.tz_convert SA01" \
-        -i "pandas.DataFrame.tz_localize SA01" \
-        -i "pandas.DataFrame.unstack RT03" \
-        -i "pandas.DataFrame.value_counts RT03" \
         -i "pandas.DataFrame.var PR01,RT03,SA01" \
         -i "pandas.DataFrame.where RT03" \
         -i "pandas.DatetimeIndex.ceil SA01" \
@@ -226,7 +223,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Index.to_list RT03" \
         -i "pandas.Index.union PR07,RT03,SA01" \
         -i "pandas.Index.unique RT03" \
-        -i "pandas.Index.value_counts RT03" \
         -i "pandas.Index.view GL08" \
         -i "pandas.Int16Dtype SA01" \
         -i "pandas.Int32Dtype SA01" \
@@ -482,10 +478,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.to_timestamp RT03,SA01" \
         -i "pandas.Series.truediv PR07" \
         -i "pandas.Series.tz_convert SA01" \
-        -i "pandas.Series.tz_localize SA01" \
-        -i "pandas.Series.unstack SA01" \
         -i "pandas.Series.update PR07,SA01" \
-        -i "pandas.Series.value_counts RT03" \
         -i "pandas.Series.var PR01,RT03,SA01" \
         -i "pandas.Series.where RT03" \
         -i "pandas.SparseDtype SA01" \
diff --git a/pandas/core/base.py b/pandas/core/base.py
index 263265701691b..f923106e967b7 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -924,6 +924,7 @@ def value_counts(
         Returns
         -------
         Series
+            Series containing counts of unique values.
 
         See Also
         --------
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 50a93994dc76b..abb8d13342c9d 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -7162,7 +7162,7 @@ def value_counts(
         dropna: bool = True,
     ) -> Series:
         """
-        Return a Series containing the frequency of each distinct row in the Dataframe.
+        Return a Series containing the frequency of each distinct row in the DataFrame.
 
         Parameters
         ----------
@@ -7175,13 +7175,14 @@ def value_counts(
         ascending : bool, default False
             Sort in ascending order.
         dropna : bool, default True
-            Don't include counts of rows that contain NA values.
+            Do not include counts of rows that contain NA values.
 
             .. versionadded:: 1.3.0
 
         Returns
         -------
         Series
+            Series containing the frequency of each distinct row in the DataFrame.
 
         See Also
         --------
@@ -7192,8 +7193,8 @@ def value_counts(
         The returned Series will have a MultiIndex with one level per input
         column but an Index (non-multi) for a single label. By default, rows
         that contain any NA values are omitted from the result. By default,
-        the resulting Series will be in descending order so that the first
-        element is the most frequently-occurring row.
+        the resulting Series will be sorted by frequencies in descending order so that
+        the first element is the most frequently-occurring row.
 
         Examples
         --------
@@ -9658,6 +9659,8 @@ def unstack(
         Returns
         -------
         Series or DataFrame
+            If index is a MultiIndex: DataFrame with pivoted index labels as new
+            inner-most level column labels, else Series.
 
         See Also
         --------
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 858d2ba82a969..ee1ce2f817b6c 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -10485,10 +10485,10 @@ def tz_localize(
         nonexistent: TimeNonexistent = "raise",
     ) -> Self:
         """
-        Localize tz-naive index of a Series or DataFrame to target time zone.
+        Localize time zone naive index of a Series or DataFrame to target time zone.
 
         This operation localizes the Index. To localize the values in a
-        timezone-naive Series, use :meth:`Series.dt.tz_localize`.
+        time zone naive Series, use :meth:`Series.dt.tz_localize`.
 
         Parameters
         ----------
@@ -10548,13 +10548,19 @@ def tz_localize(
         Returns
         -------
         {klass}
-            Same type as the input.
+            Same type as the input, with time zone naive or aware index, depending on
+            ``tz``.
 
         Raises
         ------
         TypeError
             If the TimeSeries is tz-aware and tz is not None.
 
+        See Also
+        --------
+        Series.dt.tz_localize: Localize the values in a time zone naive Series.
+        Timestamp.tz_localize: Localize the Timestamp to a timezone.
+
         Examples
         --------
         Localize local times:
diff --git a/pandas/core/series.py b/pandas/core/series.py
index b0dc05fce7913..843788273a6ef 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -4257,6 +4257,10 @@ def unstack(
         DataFrame
             Unstacked Series.
 
+        See Also
+        --------
+        DataFrame.unstack : Pivot the MultiIndex of a DataFrame.
+
         Notes
         -----
         Reference :ref:`the user guide <reshaping.stacking>` for more examples.