A K-D Tree (short for 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 halves, alternating between different dimensions at each level of the tree. Each node in a K-D Tree represents a point in the space, and the tree structure allows for efficient querying. By narrowing down the search space at each level, it significantly reduces the number of comparisons needed to find points that meet specific criteria, making it a powerful tool in computational geometry and machine learning.