diff --git a/flaml/model.py b/flaml/model.py
index 4580a3892..e1941d5f1 100644
--- a/flaml/model.py
+++ b/flaml/model.py
@@ -921,7 +921,16 @@ class TransformersEstimatorModelSelection(TransformersEstimator):
 
 
 class SKLearnEstimator(BaseEstimator):
-    """The base class for tuning scikit-learn estimators."""
+    """
+    The base class for tuning scikit-learn estimators.
+
+    Subclasses can modify the function signature of ``__init__`` to
+    ignore the values in ``config`` that are not relevant to the constructor
+    of their underlying estimator. For example, some regressors in ``scikit-learn``
+    don't accept the ``n_jobs`` parameter contained in ``config``. For these,
+    one can add ``n_jobs=None,`` before ``**config`` to make sure ``config`` doesn't
+    contain an ``n_jobs`` key.
+    """
 
     def __init__(self, task="binary", **config):
         super().__init__(task, **config)
diff --git a/website/docs/Use-Cases/Task-Oriented-AutoML.md b/website/docs/Use-Cases/Task-Oriented-AutoML.md
index ec885b345..84c1b1ea0 100644
--- a/website/docs/Use-Cases/Task-Oriented-AutoML.md
+++ b/website/docs/Use-Cases/Task-Oriented-AutoML.md
@@ -169,7 +169,7 @@ class MyRegularizedGreedyForest(SKLearnEstimator):
         return space
 ```
 
-In the constructor, we set `self.estimator_class` as `RGFClassifier` or `RGFRegressor` according to the task type. If the estimator you want to tune does not have a scikit-learn style `fit()` and `predict()` API, you can override the `fit()` and `predict()` function of `flaml.model.BaseEstimator`, like [XGBoostEstimator](../reference/model#xgboostestimator-objects).
+In the constructor, we set `self.estimator_class` as `RGFClassifier` or `RGFRegressor` according to the task type. If the estimator you want to tune does not have a scikit-learn style `fit()` and `predict()` API, you can override the `fit()` and `predict()` function of `flaml.model.BaseEstimator`, like [XGBoostEstimator](../reference/model#xgboostestimator-objects). Importantly, we also add the `task="binary"` parameter in the signature of `__init__` so that it doesn't get grouped together with the `**config` kwargs that determines the parameters with which the underlying estimator (`self.estimator_class`) is constructed. If your estimator doesn't use one of the parameters that it is passed, for example some regressors in `scikit-learn` don't use the `n_jobs` parameter, it is enough to add `n_jobs=None` to the signature so that it is ignored by the `**config` dict.
 
 2. Give the custom estimator a name and add it in AutoML. E.g.,