Transfer learning is a machine learning technique where a model developed for one task is reused for a different but related task. Instead of starting from scratch, the model leverages knowledge gained from previous training, which can significantly reduce the time and data needed for training on the new task. This approach is particularly useful in situations where labeled data is scarce. For example, a model trained to recognize objects in images can be adapted to identify specific types of animals with minimal additional training. This efficiency makes transfer learning a popular choice in fields like computer vision and natural language processing.