Data augmentation is a crucial technique in machine learning that addresses the problem of overfitting by artificially expanding training datasets through modifications of existing data. It enhances model performance, particularly in deep learning, by creating diverse and unique data samples. This approach is not limited to preventing overfitting but also improves model accuracy when the initial dataset is insufficient. Common data types subjected to augmentation include images, audio, and text, with various techniques applied such as geometric transformations for images, noise injection for audio, and word shuffling for text. Deep learning frameworks like TensorFlow, PyTorch, and MxNet offer built-in augmentation libraries, while custom libraries like Albumentations and ImgAug provide extensive transformation methods. Despite its benefits, data augmentation requires careful application to avoid introducing irrelevant variations, and speed comparisons show Albumentations and Transforms as efficient choices for image augmentation tasks.