Data augmentation is a technique used in machine learning to increase the diversity of training data without actually collecting new data. It involves creating modified versions of existing data samples by applying various transformations, such as rotation, scaling, flipping, or adding noise. This helps improve the model's ability to generalize and perform better on unseen data. By using data augmentation, models can learn to recognize patterns more effectively, as they are exposed to a wider range of variations. This is particularly useful in fields like computer vision and natural language processing, where having a large and diverse dataset is crucial for training robust models.