A data scientist is tuning a Spark ML GBTRegressor on a large dataset (over 50 million rows). Because k-fold cross-validation would be too expensive, they decide to use TrainValidationSplit instead. They want 80% of the data used for training each model and 20% held out for validating each hyperparameter combination. Which configuration correctly achieves this?