Transfer Learning is a machine learning technique where a pre-trained model, initially developed for a certain problem, is adapted for a different but related task. This approach leverages prior knowledge gained from the first task, allowing for faster and usually more efficient learning in the new domain. For example, a model trained on animal images can be fine-tuned to recognize different breeds of dogs, saving time and computational resources.