k-dimensional Trees, or k-d Trees, are data structures used for organizing points in a k-dimensional space. They enable efficient searching, insertion, and deletion of points, making them useful in applications like computer graphics, machine learning, and geospatial analysis. Each node in a k-d tree represents a point, and the tree is split into two halves based on a chosen dimension, allowing for quick access to multidimensional data. The construction of a k-d tree involves recursively dividing the data points along different dimensions. This process continues until each leaf node contains a single point. The resulting structure allows for efficient range searches and nearest neighbor searches, significantly improving performance compared to linear search methods.