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 performance of models by making them more robust and better at generalizing to new, unseen data. By using Data Augmentation, researchers and developers can enhance the quality of their datasets, especially when they have limited data available. This approach is particularly useful in fields like computer vision and natural language processing, where having a large and varied dataset is crucial for training effective models.