Data augmentation is a technique used in machine learning to improve model performance by artificially expanding training datasets. It involves applying transformations like rotation, flipping, scaling, cropping, or color adjustments to existing data, creating new variations. Primarily used in computer vision and NLP tasks, data augmentation helps models generalize better by exposing them to diverse inputs. For instance, in image recognition, flipping or rotating an image mimics real-world scenarios, reducing overfitting and improving accuracy without collecting additional data.