The Interior Point Method is an algorithmic framework used to solve linear and nonlinear convex optimization problems by traversing the interior of the feasible region. It contrasts with the Simplex Method by avoiding the boundary of the feasible region, often resulting in more efficient solutions for large-scale problems.