In each iteration of the optimization, fitrsvm is called with 5-fold crossvalidation, using a particular vector of hyperparameters. This results in a RegressionPartitionedSVM. Then the kfoldLoss method is called on that object, obtaining the Loss for that vector of hyperparameters. That Loss value is printed in the command line display in the "Objective" column for that iteration.
In the next iteration, a new vector of hyperparameters is chosen, and the process is repeated.
Finally, after 30 (by default) iterations, the "best" hyperparameter vector is chosen, and a final model is trained on the entire dataset using those hyperparameters, without crossvalidation. That final RegressionSVM model is returned.