Image Augmentation is a technique used in machine learning and computer vision to enhance the diversity of training datasets. By applying various transformations to existing images, such as rotation, flipping, scaling, or color adjustments, new images are generated. This helps improve the performance of models by providing them with more varied examples to learn from. The primary goal of Image Augmentation is to prevent overfitting, where a model learns to perform well on training data but fails to generalize to new, unseen data. By artificially expanding the dataset, models can better recognize patterns and features, leading to improved accuracy and robustness in tasks like image classification and object detection.