The use of design of experiment (DoE) and data-driven simulation has become state-of-the-art in engine development and base calibration to cope with the drastically increased complexity of today's engine ECUs (electronic control units). Based on the representation of the engine behavior with a virtual plant model, offline optimizers can be used to find the optimal calibration settings for the engine controller, e.g. with respect to fuel consumption and exhaust gas emissions. This increases the efficiency of the calibration process and reduces the need for expensive test stand runs. The present paper describes the application of Gaussian process regression, a statistical modeling approach with practical benefits in terms of achievable model accuracy and usability. The implementation of the algorithm in a commercial tool framework enables a broad use in series engine calibration. Recent developments have extended the approach towards dynamic systems identification and simulation of transient behavior. Due to the data-driven nature, the generated plant models can further be used to replace time-consuming 1-D simulations (meta-modeling) without loss in model quality while meeting real-time requirements, e.g. for utilization in hardware-in-the-loop (HiL) environments. The application and benefits of the statistical modeling approach are shown on several examples.