Data augmentation is a technique for artificially increasing the size and diversity of a training dataset by creating modified versions of existing data. This is especially common in machine learning fields like computer vision to improve model performance. The main reason to use data augmentation is to help a model generalize better to new, unseen data. By exposing the model to a wider variety of training examples, it becomes more robust and less likely to overfit. Overfitting occurs when a model learns the training data too well, including its noise and irrelevant details, which harms its performance on new data. Data augmentation effectively increases the dataset size and acts as a regularizer to combat this issue.