Models of small CPS and IoT applications often use approximated values that describe physical system behaviour. Physical resources, such as electricity consumption and heating power, have to be estimated, since many off-the-shelf components lack the required descriptions. Controllers which are based on these approximations can hence use imprecise models, perform misleading simulation, and cause damaged systems and financial loss. In this paper we present ML4CREST, a machine learning approach to automatically calibrate models using sensor measurements. We show that our approach is well-suited for the calibration of the flow rates within an automated watering system, which allows precise simulation and prevents spillage.