Discuss the theoretical foundations of Stochastic Approximation, focusing on the Robbins-Monro conditions. Explain how these conditions regarding the step size (learning rate) ensure convergence in Stochastic Gradient Descent (SGD) despite the noise introduced by random sampling of the gradient.