Data augmentation is a powerful technique used in machine learning and deep learning to enhance the size and diversity of training datasets without the need for additional data collection. By applying various transformations to existing data, such as flipping, rotating, or cropping images, models can learn to generalize better and improve their performance. This is particularly beneficial in fields like computer vision, where having more varied examples helps models recognize patterns more effectively. Ultimately, data augmentation allows for more robust models that can perform well on unseen data, making it a crucial strategy in the development of machine learning applications.