In transfer learning, what and how to transfer are two primary issues to be addressed. Different transfer learning algorithms applied between a source and a target domain result in different knowledge transferred, as well as the performance improvement in the target domain. Determining the optimal one that maximizes the performance improvement requires exhaustive exploration of all existing transfer learning algorithms, which is computationally intractable. As a trade-off, researchers with considerable expertise select a sub-optimal algorithm. Meanwhile, it is widely accepted in educational psychology that human beings improve transfer learning skills of deciding what to transfer through meta-cognitive reflection on inductive transfer learning practices. Motivated by this, we propose a novel transfer learning framework known as Learning to Transfer (L2T) to automatically determine what and how to transfer are the best by leveraging previous transfer learning experiences. We establish the L2T framework in two stages: 1) we learn a reflection function encrypting transfer learning skills from experiences; and 2) we infer what and how to transfer are the best for a future pair of domains by optimizing the reflection function. We also theoretically analyse the algorithmic stability and generalization bound of L2T, and empirically demonstrate its superiority over several state-of the-art transfer learning algorithms.