Data augmentation involves applying a set of transformations to the existing dataset to artificially expand it and improve the diversity of the training data. Examples include rotating, cropping, or flipping images in image processing, or altering text slightly in natural language tasks. By using data augmentation, models become more robust against overfitting and improve their generalization ability, making them more adaptable to variations in input data.