diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 06078d8958492..2aa256b65a493 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -116,7 +116,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Timestamp.resolution PR02" \
         -i "pandas.Timestamp.tzinfo GL08" \
         -i "pandas.Timestamp.year GL08" \
-        -i "pandas.api.extensions.ExtensionArray.interpolate PR01,SA01" \
         -i "pandas.api.types.is_dict_like PR07,SA01" \
         -i "pandas.api.types.is_extension_array_dtype SA01" \
         -i "pandas.api.types.is_file_like PR07,SA01" \
diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index 536c7303a2f92..a933a9ce11646 100644
--- a/pandas/core/arrays/base.py
+++ b/pandas/core/arrays/base.py
@@ -999,16 +999,74 @@ def interpolate(
         **kwargs,
     ) -> Self:
         """
-        See DataFrame.interpolate.__doc__.
+        Fill NaN values using an interpolation method.
+
+        Parameters
+        ----------
+        method : str, default 'linear'
+            Interpolation technique to use. One of:
+            * 'linear': Ignore the index and treat the values as equally spaced.
+            This is the only method supported on MultiIndexes.
+            * 'time': Works on daily and higher resolution data to interpolate
+            given length of interval.
+            * 'index', 'values': use the actual numerical values of the index.
+            * 'pad': Fill in NaNs using existing values.
+            * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric',
+            'polynomial': Passed to scipy.interpolate.interp1d, whereas 'spline'
+            is passed to scipy.interpolate.UnivariateSpline. These methods use
+            the numerical values of the index.
+            Both 'polynomial' and 'spline' require that you also specify an
+            order (int), e.g. arr.interpolate(method='polynomial', order=5).
+            * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima',
+            'cubicspline': Wrappers around the SciPy interpolation methods
+            of similar names. See Notes.
+            * 'from_derivatives': Refers to scipy.interpolate.BPoly.from_derivatives.
+        axis : int
+            Axis to interpolate along. For 1-dimensional data, use 0.
+        index : Index
+            Index to use for interpolation.
+        limit : int or None
+            Maximum number of consecutive NaNs to fill. Must be greater than 0.
+        limit_direction : {'forward', 'backward', 'both'}
+            Consecutive NaNs will be filled in this direction.
+        limit_area : {'inside', 'outside'} or None
+            If limit is specified, consecutive NaNs will be filled with this
+            restriction.
+            * None: No fill restriction.
+            * 'inside': Only fill NaNs surrounded by valid values (interpolate).
+            * 'outside': Only fill NaNs outside valid values (extrapolate).
+        copy : bool
+            If True, a copy of the object is returned with interpolated values.
+        **kwargs : optional
+            Keyword arguments to pass on to the interpolating function.
+
+        Returns
+        -------
+        ExtensionArray
+            An ExtensionArray with interpolated values.
+
+        See Also
+        --------
+        Series.interpolate : Interpolate values in a Series.
+        DataFrame.interpolate : Interpolate values in a DataFrame.
+
+        Notes
+        -----
+        - All parameters must be specified as keyword arguments.
+        - The 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima'
+          methods are wrappers around the respective SciPy implementations of
+          similar names. These use the actual numerical values of the index.
 
         Examples
         --------
+        Interpolating values in a NumPy array:
+
         >>> arr = pd.arrays.NumpyExtensionArray(np.array([0, 1, np.nan, 3]))
         >>> arr.interpolate(
         ...     method="linear",
         ...     limit=3,
         ...     limit_direction="forward",
-        ...     index=pd.Index([1, 2, 3, 4]),
+        ...     index=pd.Index(range(len(arr))),
         ...     fill_value=1,
         ...     copy=False,
         ...     axis=0,
@@ -1017,6 +1075,22 @@ def interpolate(
         <NumpyExtensionArray>
         [0.0, 1.0, 2.0, 3.0]
         Length: 4, dtype: float64
+
+        Interpolating values in a FloatingArray:
+
+        >>> arr = pd.array([1.0, pd.NA, 3.0, 4.0, pd.NA, 6.0], dtype="Float64")
+        >>> arr.interpolate(
+        ...     method="linear",
+        ...     axis=0,
+        ...     index=pd.Index(range(len(arr))),
+        ...     limit=None,
+        ...     limit_direction="both",
+        ...     limit_area=None,
+        ...     copy=True,
+        ... )
+        <FloatingArray>
+        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
+        Length: 6, dtype: Float64
         """
         # NB: we return type(self) even if copy=False
         raise NotImplementedError(