Data augmentation is a powerful technique used to enhance the size and diversity of training datasets for machine learning models. By applying transformations like rotation, flipping, cropping, scaling, and adding noise to existing data, it helps improve model accuracy and robustness. In domains like image processing, NLP, and audio analysis, data augmentation mitigates overfitting and allows models to generalize better on unseen data. This cost-effective approach is crucial for scenarios with limited datasets, enabling AI systems to perform reliably across a wide range of real-world conditions.