Data augmentation is a technique used in AI and machine learning to improve model performance by artificially increasing the amount of training data. It involves making small modifications to existing data, such as rotating images, changing colors, or adding noise to text or audio. This helps models learn more effectively, become more robust, and generalize better to new data while reducing biases and overfitting.