Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alternative. However, due to the computational cost of these high fidelity simulations, the use of neural networks, kernel methods, and other surrogate modeling techniques have become indispensable. Surrogate models are compact and cheap to evaluate, and have proven very useful for tasks such as optimization, design space exploration, prototyping, and sensitivity analysis. Consequently, in many scientific fields there is great interest in techniques that facilitate the construction of such regression models, while minimizing the computational cost and maximizing model accuracy. This paper presents a fully automated machine learning toolkit for regression modeling and active learning to tackle these issues. A strong focus is placed on adaptivity, self-tuning and robustness in order to maximize efficiency and make the algorithms and tools easily accessible to other scientists in computational science and engineering.