Hyperparameter tuning is the process of finding the optimal settings of the training hyperparameters, so that we minimize the training time and maximize the test accuracy. Usually, these two objectives can’t be fully optimized. If we minimize the training time, we likely will not achieve the best accuracy. Likewise, if we maximize the test accuracy, we likely will need longer to train.
Tuning is finding the combination of hyperparameter settings that meet your targets for the objectives. For example, if your target is the highest possible accuracy, you may not concern yourself with minimizing the training time. In another situation, if you need only good (but not the best) accuracy, and you are continuously retraining, you may want to find settings that get this good accuracy while minimizing the training time.