diff --git a/doc/python/python_api.rst b/doc/python/python_api.rst
index 03b431c77d41..b27542a8becb 100644
--- a/doc/python/python_api.rst
+++ b/doc/python/python_api.rst
@@ -173,3 +173,13 @@ PySpark API
     :members:
     :inherited-members:
     :show-inheritance:
+
+.. autoclass:: xgboost.spark.SparkXGBRanker
+    :members:
+    :inherited-members:
+    :show-inheritance:
+
+.. autoclass:: xgboost.spark.SparkXGBRankerModel
+    :members:
+    :inherited-members:
+    :show-inheritance:
diff --git a/doc/tutorials/spark_estimator.rst b/doc/tutorials/spark_estimator.rst
index 44e7a957513b..e0acc745433d 100644
--- a/doc/tutorials/spark_estimator.rst
+++ b/doc/tutorials/spark_estimator.rst
@@ -43,10 +43,10 @@ in spark estimator, and some parameters are replaced with pyspark specific param
 such as `weight_col`, `validation_indicator_col`, `use_gpu`, for details please see
 `SparkXGBRegressor` doc.
 
-The following code snippet shows how to train a spark xgboost regressor model,
-first we need to prepare a training dataset as a spark dataframe contains
-"label" column and "features" column(s), the "features" column(s) must be `pyspark.ml.linalg.Vector`
-type or spark array type or a list of feature column names.
+The following code snippet shows how to train a spark xgboost regressor model, first we
+need to prepare a training dataset as a spark dataframe contains "label" column and
+"features" column(s), the "features" column(s) must be ``pyspark.ml.linalg.Vector`` type
+or spark array type or a list of feature column names.
 
 
 .. code-block:: python
@@ -54,10 +54,10 @@ type or spark array type or a list of feature column names.
   xgb_regressor_model = xgb_regressor.fit(train_spark_dataframe)
 
 
-The following code snippet shows how to predict test data using a spark xgboost regressor model,
-first we need to prepare a test dataset as a spark dataframe contains
-"features" and "label" column, the "features" column must be `pyspark.ml.linalg.Vector`
-type or spark array type.
+The following code snippet shows how to predict test data using a spark xgboost regressor
+model, first we need to prepare a test dataset as a spark dataframe contains "features"
+and "label" column, the "features" column must be ``pyspark.ml.linalg.Vector`` type or
+spark array type.
 
 .. code-block:: python
 
diff --git a/python-package/xgboost/spark/__init__.py b/python-package/xgboost/spark/__init__.py
index c0b347eefb30..2720532c0411 100644
--- a/python-package/xgboost/spark/__init__.py
+++ b/python-package/xgboost/spark/__init__.py
@@ -1,6 +1,5 @@
 # type: ignore
-"""PySpark XGBoost integration interface
-"""
+"""PySpark XGBoost integration interface"""
 
 try:
     import pyspark
@@ -11,6 +10,7 @@
     SparkXGBClassifier,
     SparkXGBClassifierModel,
     SparkXGBRanker,
+    SparkXGBRankerModel,
     SparkXGBRegressor,
     SparkXGBRegressorModel,
 )
@@ -21,4 +21,5 @@
     "SparkXGBRegressor",
     "SparkXGBRegressorModel",
     "SparkXGBRanker",
+    "SparkXGBRankerModel",
 ]