Transfer learning is a machine learning technique that leverages knowledge gained from one task to enhance the performance of a model on a different, yet related task. This approach is particularly beneficial in deep learning, where training models from scratch can be resource-intensive and time-consuming. By utilizing pre-trained models, transfer learning allows practitioners to achieve better results with less labeled data and reduced training time. It is commonly applied in fields such as computer vision and natural language processing, where the insights from one domain can significantly improve learning in another related domain.