diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index c3ee7a4e262fc..50baa1cb0b19f 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -173,8 +173,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.core.groupby.DataFrameGroupBy.transform\
         pandas.core.groupby.DataFrameGroupBy.nth\
         pandas.core.groupby.DataFrameGroupBy.rolling\
-        pandas.core.groupby.SeriesGroupBy.idxmax\
-        pandas.core.groupby.SeriesGroupBy.idxmin\
         pandas.core.groupby.SeriesGroupBy.nth\
         pandas.core.groupby.SeriesGroupBy.rolling\
         pandas.core.groupby.DataFrameGroupBy.hist\
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 1a23d237dca46..d37acd8312483 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -1042,12 +1042,104 @@ def nsmallest(
         result = self._python_apply_general(f, data, not_indexed_same=True)
         return result
 
-    @doc(Series.idxmin.__doc__)
     def idxmin(self, skipna: bool = True) -> Series:
+        """
+        Return the row label of the minimum value.
+
+        If multiple values equal the minimum, the first row label with that
+        value is returned.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Exclude NA/null values. If the entire Series is NA, the result
+            will be NA.
+
+        Returns
+        -------
+        Index
+            Label of the minimum value.
+
+        Raises
+        ------
+        ValueError
+            If the Series is empty.
+
+        See Also
+        --------
+        numpy.argmin : Return indices of the minimum values
+            along the given axis.
+        DataFrame.idxmin : Return index of first occurrence of minimum
+            over requested axis.
+        Series.idxmax : Return index *label* of the first occurrence
+            of maximum of values.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+
+        >>> ser.groupby(['a', 'a', 'b', 'b']).idxmin()
+        a   2023-01-01
+        b   2023-02-01
+        dtype: datetime64[ns]
+        """
         return self._idxmax_idxmin("idxmin", skipna=skipna)
 
-    @doc(Series.idxmax.__doc__)
     def idxmax(self, skipna: bool = True) -> Series:
+        """
+        Return the row label of the maximum value.
+
+        If multiple values equal the maximum, the first row label with that
+        value is returned.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Exclude NA/null values. If the entire Series is NA, the result
+            will be NA.
+
+        Returns
+        -------
+        Index
+            Label of the maximum value.
+
+        Raises
+        ------
+        ValueError
+            If the Series is empty.
+
+        See Also
+        --------
+        numpy.argmax : Return indices of the maximum values
+            along the given axis.
+        DataFrame.idxmax : Return index of first occurrence of maximum
+            over requested axis.
+        Series.idxmin : Return index *label* of the first occurrence
+            of minimum of values.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+
+        >>> ser.groupby(['a', 'a', 'b', 'b']).idxmax()
+        a   2023-01-15
+        b   2023-02-15
+        dtype: datetime64[ns]
+        """
         return self._idxmax_idxmin("idxmax", skipna=skipna)
 
     @doc(Series.corr.__doc__)