Most of the ML model representations have large hypothesis spaces. A sequential search of all possibilities would take longer than you would ever want to wait for an answer; months, years, or lifetimes, depending on the complexity of the model representation. The choice of the optimization method determines the efficiency of the learning process. There are several general methods to search for the optimal classifier. Some examples are gradient descent, greedy search, and linear programming.