Motivation for gradient descent method over OLS/MLE for simple linear regression?

Question

I am beginner in machine learning and I am currently trying to find the motivation for gradient descent method. I am confused why we want to employ gradient descent method for linear regression? I see the cost function the same as the OLS function, and gradient descent method here actually takes more effort than simply getting the derivatives equal one. Then why we always try to use gradient descent here? I am when the model gets more complicated , and also when we make more assumptions on the prior distribution of the theta(parameters). The optimization problem will become much more complicated. Then will gradient descent method still survive in terms of this? And OLS/MLE method will not be able to predict the parameters? I see OLS as minimize the cost, and the MLE method as maximize the prob, which is in essence the same.(reference http://www.cs.ubc.ca/~nando/540-2013/lectures/l3.pdf) Should I think gradient descent method as a improvement from the OLS method, while the E-M method(maximize the expected likelihood) as a imporvement from the MLE method. Thanks in advance!