diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index f2d9f582d8932..ca336fdebabc0 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -119,7 +119,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.api.types.is_hashable PR01,RT03,SA01" \
         -i "pandas.api.types.is_int64_dtype SA01" \
         -i "pandas.api.types.is_integer PR01,SA01" \
-        -i "pandas.api.types.is_interval_dtype SA01" \
         -i "pandas.api.types.is_iterator PR07,SA01" \
         -i "pandas.api.types.is_list_like SA01" \
         -i "pandas.api.types.is_named_tuple PR07,SA01" \
diff --git a/pandas/core/dtypes/common.py b/pandas/core/dtypes/common.py
index ff855f97a352b..1b388b9f4acf4 100644
--- a/pandas/core/dtypes/common.py
+++ b/pandas/core/dtypes/common.py
@@ -479,6 +479,15 @@ def is_interval_dtype(arr_or_dtype) -> bool:
     boolean
         Whether or not the array-like or dtype is of the Interval dtype.
 
+    See Also
+    --------
+    api.types.is_object_dtype : Check whether an array-like or dtype is of the
+        object dtype.
+    api.types.is_numeric_dtype : Check whether the provided array or dtype is
+        of a numeric dtype.
+    api.types.is_categorical_dtype : Check whether an array-like or dtype is of
+        the Categorical dtype.
+
     Examples
     --------
     >>> from pandas.core.dtypes.common import is_interval_dtype