Data Augmentation involves techniques to increase the diversity of training data by applying transformations to existing data samples. Methods such as rotation, scaling, and cropping for images, or paraphrasing and synonym replacement for text, create variations of the original data without collecting new samples. Data augmentation helps improve the generalization and robustness of machine learning models by exposing them to a wider range of scenarios and variations, which can lead to better performance and reduced overfitting.