Use config_context in sklearn interface. (#8141)

doc/tutorials/dask.rst

@@ -115,7 +115,7 @@ Alternatively, XGBoost also implements the Scikit-Learn interface with
 :py:class:`~xgboost.dask.DaskXGBRanker` and 2 random forest variances.  This wrapper is
 similar to the single node Scikit-Learn interface in xgboost, with dask collection as
 inputs and has an additional ``client`` attribute.  See following sections and
-:ref:`sphx_glr_python_dask-examples` for more examples.
+:ref:`dask-examples` for more examples.
 
 
 ******************

doc/tutorials/index.rst

@@ -16,6 +16,7 @@ See `Awesome XGBoost <https://github.com/dmlc/xgboost/tree/master/demo>`_ for mo
   Distributed XGBoost with XGBoost4J-Spark <https://xgboost.readthedocs.io/en/latest/jvm/xgboost4j_spark_tutorial.html>
   Distributed XGBoost with XGBoost4J-Spark-GPU <https://xgboost.readthedocs.io/en/latest/jvm/xgboost4j_spark_gpu_tutorial.html>
   dask
+  spark_estimator
   ray
   dart
   monotonic