Knowledge Distillation is a technique where a smaller, more efficient model (the student) is trained to mimic the performance of a larger, more complex model (the teacher). By transferring knowledge from the teacher to the student, this approach helps achieve high accuracy with reduced computational resources and model size. Knowledge distillation is valuable for deploying models in resource-constrained environments and improving inference efficiency while maintaining performance.