A k-dimensional tree, or k-d tree, is a data structure used for organizing points in a k-dimensional space. It is particularly useful for applications like multidimensional search and nearest neighbor search. The tree is constructed by recursively splitting the space into two halves based on the values of the points along different dimensions. Each node in a k-d tree represents a point, and the tree is built by choosing a dimension to split on at each level. This allows efficient querying of points, as it reduces the number of comparisons needed to find nearby points or to search for specific coordinates in the space.