Knowledge Distillation involves training a smaller model, called the 'student', to reproduce the behavior of a larger, possibly more complex 'teacher' model. The teacher model, which has been trained with significant computational resources, encompasses high-level knowledge. By simplifying this knowledge, the student model delivers similar predictions with lesser computational demands. This approach enhances model efficiency, making it feasible to deploy models on devices with constrained resources like smartphones. It is related to transfer learning as both techniques involve leveraging pre-learned models.