Data augmentation is a powerful technique used in machine learning, particularly in image classification tasks, to enhance the diversity of training datasets without the need for additional data collection. By applying random transformations—such as rotation, flipping, and scaling—data augmentation generates new, synthetic examples from existing data. This process helps models generalize better by teaching them to recognize patterns despite variations in input. As a result, data augmentation can significantly improve the performance of machine learning models, making them more robust and effective in real-world applications.