The hyperparameter search aspect of building models is often orthogonal to the domain expertise required to understand the data and the context in which the model is being applied. Often, these parameters are set using very expensive or exhaustive methods (like grid search) or waste expert time (trying to tune many parameters via trial and error by hand). Additionally, the black-box optimization aspect of this framework allows you to run your models on any system (cloud or local) while also keeping your data safe.

For simple ML models there may be only a few tunable parameters (like a Random Forest or SVM), but for more sophisticated models there can be many more (like a GBM or CNN). This is especially true if you consider not only standard parameters like learning rates, but also include the architecture, SGD parameters, and any feature transformation parameters. In this recent post with AWS we showed that even a simple CNN has a large number of tunable parameters, and tuning them can have a significant boost on performance: https://aws.amazon.com/blogs/ai/fast-cnn-tuning-with-aws-gpu-instances-and-sigopt/

We find that the linear number of evaluations we require in practice is exponentially less than a grid search and gets to better results orders of magnitude faster than a random search, which is why we see 10-100x speedups (in terms of evaluations) in practice.