Transfer Learning is a machine learning technique where a model developed for one task is reused for a different but related task. This approach allows the model to leverage knowledge gained from the first task, making it easier and faster to train on the new task. It is particularly useful when there is limited data available for the new task. In deep learning, transfer learning often involves using a pre-trained model, such as those trained on large datasets like ImageNet, and fine-tuning it for a specific application. This method can significantly reduce training time and improve performance, especially in fields like computer vision and natural language processing.