Data augmentation is a technique used in machine learning to artificially expand a dataset by applying transformations such as rotation, scaling, flipping, or noise injection. It helps improve model performance by increasing data diversity, particularly in image, speech, and text recognition tasks. Data augmentation is essential in deep learning to reduce overfitting and enhance model generalization.