A ball tree is a data structure used in computer science for organizing points in a multi-dimensional space. It helps in efficiently searching for nearest neighbors, which is useful in various applications like machine learning and computer vision. The structure divides the space into nested hyperspheres, or "balls," where each ball contains a subset of points. The main advantage of ball trees is their ability to reduce the number of distance calculations needed when searching for nearby points. This makes them particularly effective for high-dimensional data, where traditional methods, such as k-d trees, may struggle with performance.