A k-d tree, or k-dimensional tree, is a data structure used for organizing points in a k-dimensional space. It is particularly useful for tasks like nearest neighbor searches and range searches. The tree is built by recursively splitting the space into two halves based on the values of the points along different dimensions, allowing efficient querying of spatial data. Each node in a k-d tree represents a point, and the tree alternates between dimensions at each level. For example, in a 2D space, the first split might be along the x-axis, followed by a split along the y-axis at the next level. This structure helps in quickly narrowing down potential candidates for queries.