Knowledge distillation improves model efficiency by transferring knowledge from a large, complex model to a smaller, more efficient model. The smaller model, known as the student model, learns to mimic the behavior of the larger teacher model while using fewer parameters and less computational resources. This results in a more efficient model that maintains high performance.