Orthogonal projection is a linear transformation that maps a vector onto a subspace in such a way that the error, or the difference between the vector and its projection, is minimized and orthogonal to the subspace. This concept is fundamental in linear algebra and is widely applied in fields such as computer graphics, signal processing, and statistics for dimensionality reduction and data approximation.