At each step that you are asked to make a decision (i.e. choose one option among several options), you must have an additional set/partition to gauge the accuracy of your choice so that you do not simply pick the most favorable result of randomness and mistake the tail-end of the distribution for the center 1. The left is the pessimist. The right is the optimist. The center is the pragmatist. Be the pragmatist.
Step 1) Training: Each type of algorithm has its own parameter options (the number of layers in a Neural Network, the number of trees in a Random Forest, etc). For each of your algorithms, you must pick one option. That’s why you have a training set.
Step 2) Validating: You now have a collection of algorithms. You must pick one algorithm. That’s why you have a test set. Most people pick the algorithm that performs best on the validation set (and that's ok). But, if you do not measure your top-performing algorithm’s error rate on the test set, and just go with its error rate on the validation set, then you have blindly mistaken the “best possible scenario” for the “most likely scenario.” That's a recipe for disaster.
Step 3) Testing: I suppose that if your algorithms did not have any parameters then you would not need a third step. In that case, your validation step would be your test step. Perhaps Matlab does not ask you for parameters or you have chosen not to use them and that is the source of your confusion.
1 It is often helpful to go into each step with the assumption (null hypothesis) that all options are the same (e.g. all parameters are the same or all algorithms are the same), hence my reference to the distribution.
2 This image is not my own. I have taken it from this site: http://www.teamten.com/lawrence/writings/bell-curve.png