Orthogonal projection is a mathematical process used to find the closest point on a given subspace from a point in a higher-dimensional space. This involves dropping a perpendicular line from the point to the subspace, ensuring that the distance is minimized. The result is a point in the subspace that represents the best approximation of the original point. In geometry, orthogonal projection is often visualized in Euclidean space, where it helps in simplifying complex problems. It is widely used in various fields, including computer graphics, data analysis, and machine learning, to reduce dimensions and improve computational efficiency.