Data augmentation is a technique used to expand and diversify a training dataset by applying various modifications to existing data. These modifications can include flipping, rotation, cropping, scaling, adding noise, or adjusting brightness and contrast. By creating new variations of the data, data augmentation helps the model generalize better and improve performance. It is particularly useful when the training data is limited, preventing overfitting and enhancing the model's ability to handle different scenarios. By introducing more diverse examples, data augmentation boosts the model's robustness and adaptability, enabling it to make accurate predictions on unseen or slightly modified data.