While both methods are used to evaluate machine learning models, a cross-validation involves testing the machine learning model on multiple train-test data splits, whereas a train-test data split involves one random split of the original train and test dataset. Cross-validation provides a better estimate of the model's performance on unseen data and helps in analyzing underfitting or overfitting.