Passing metadata such as sample_weight and groups through a scikit-learn cross_validate, GridSearchCV, or a Pipeline to the right estimators, scorers, and CV splitters has been either cumbersome, hacky, or impossible. The new metadata routing mechanism in scikit-learn enables you to pass metadata through these objects. As a use-case, we study how you can implement a revenue sensitive scoring while doing a hyperparameter search within a GridSearchCV object.