If i remember correctly, it does do that for fast ai but i couldnt find that in the code. However to answer your question, data augmentation not only helps in getting more data but helps the model generalize better by preventing the model from learning too much from the data (also known as overfitting). This means that your model will perform better on the unseen data or future data that may not be perfectly curated like your training data.
A good example comes from image recognition. You can curate the data easily for training but the normal images that you get online or some images that people click may not be centered or have an angle depending on how they hold the camera. This helps the model in predicting these type of real world images much better.