A KD-Tree, or k-dimensional tree, is a data structure used for organizing points in a k-dimensional space. It is particularly useful for applications involving multidimensional search keys, such as range searches and nearest neighbor searches. The tree is constructed by recursively splitting the space into two half-spaces, alternating between different dimensions at each level of the tree. Each node in a KD-Tree represents a point in the space, and the tree structure allows for efficient querying. By narrowing down the search space at each level, KD-Trees can significantly reduce the number of comparisons needed to find points that meet specific criteria, making them valuable in fields like computer graphics and machine learning.