Data augmentation is a technique used in machine learning and data analysis to increase the size and diversity of a dataset by creating modified versions of existing data points. This can involve applying transformations such as cropping, flipping, rotating, or adjusting brightness to images, or adding noise to text or numerical data. By augmenting the dataset with these variations, the model is exposed to a wider range of examples and can learn more effectively, leading to improved performance and generalization on unseen data. Data augmentation is commonly used in tasks such as image classification, object detection, and natural language processing.