The measure-theoretic approach to stochastic processes starts with a probability space and defines a stochastic process as a family of functions on this probability space.
The theorem is used in one of the standard proofs of existence of a Brownian motion, by specifying the finite dimensional distributions to be Gaussian random variables, satisfying the consistency conditions above.