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Surrogate models are used to map input data to output data when the actual relationship between the two is unknown or computationally expensive to evaluate for several applications, including surface approximation and surrogate-based optimization. Many techniques have been developed for surrogate modeling; however, a systematic method for selecting suitable techniques for an application remains an open challenge. This work compares the performance of eight surrogate modeling techniques for approximating a surface over a set of simulated data. Using the comparison results, we constructed a Random Forest based tool to recommend the appropriate surrogate modeling technique for a given dataset using attributes calculated only from the available input and output values. The tool identifies the appropriate surrogate modeling techniques for surface approximation with an accuracy of 87% and a precision of 86%. Using the tool for surrogate model form selection enables computational time savings by avoiding expensive trial-and-error selection methods.