Why can cross-validation provide a more robust performance estimate than a single train-test split?
It removes difficult features before every evaluation.
It evaluates the model on multiple data splits, reducing dependence on one fortunate or unfortunate split.
It trains the model directly on the test data to improve its score.
It guarantees identical performance on all unseen data.