What are the differences between a ball tree and a kd tree in terms of efficiency and performance for nearest neighbor search algorithms?

A ball tree is generally more efficient for high-dimensional data sets, while a kd tree is better suited for low-dimensional data sets. Ball trees have a faster query time for nearest neighbor searches in high-dimensional spaces, but kd trees have a faster build time and are more memory efficient for low-dimensional spaces.