Transfer Learning is a technique where a pre-trained model, developed for one task, is adapted to perform a different but related task. By leveraging the knowledge gained from previous training, transfer learning helps reduce the amount of data and computational resources needed for new tasks. This approach is particularly useful when data for the new task is limited or when training a model from scratch would be too resource-intensive. Transfer learning is widely applied in image classification, natural language processing, and other domains where pre-trained models can be fine-tuned to achieve high performance on related tasks.