The Orthogonality Principle states that the error between an estimated value and the true value is orthogonal to the space spanned by the known variables, ensuring that the projection of the error onto this space is zero. This principle is fundamental in linear estimation and signal processing, where it is used to derive optimal estimates by minimizing the mean squared error.