Data augmentation is a crucial technique in machine learning and deep learning that enhances the size and diversity of training datasets without the need for additional data collection. By applying various transformations to existing data, such as flipping, cropping, or rotating images, or modifying text in natural language processing, models can learn to generalize better and reduce the risk of overfitting. This process is particularly beneficial when dealing with limited or imbalanced datasets, as it allows for improved model performance and robustness. Overall, data augmentation plays a vital role in developing effective machine learning solutions across various domains.