A single train-test split is always sufficient regardless of dataset size

Cross-validation eliminates the need for a separate test set entirely

Cross-validation always produces higher accuracy numbers

Cross-validation uses all data for both training and testing, providing a more reliable and less variable performance estimate on small datasets