Modeling uncertainties pose a significant challenge in the development and deployment of model-based vehicle control systems. Most model- based automotive control systems require the use of a well estimated vehicle dynamics prediction model. The ability of first principles-based models to represent vehicle behavior becomes limited under complex scenarios due to underlying rigid physical assumptions. Additionally, the increasing complexity of these models to meet ever-increasing fidelity requirements presents challenges for obtaining analytical solutions as well as control design. Alternatively, deterministic data driven techniques including but not limited to deep neural networks, polynomial regression, Sparse Identification of Nonlinear Dynamics (SINDy) have been deployed for vehicle dynamics system identification and prediction. However, under real-world conditions which are often uncertain or time varying, including, but not limited to changing terrain and/or physical, a single time-invariant physics- based or parametric model may not accurately represent vehicle behavior resulting in sub-optimal controller performance. The previously mentioned data-driven system identification techniques, by virtue of being deterministic cannot express these uncertainties, leading to a need for multiple models, or a distribution of models to describe vehicle behavior. Gaussian Process Regression constitutes a cogent approach for capturing and expressing modeling uncertainties through a probability distribution. In this paper, we demonstrate Gaussian Process Regression as an able technique for modeling uncertain vehicle dynamics using a real-world vehicle dataset, acquired by performing benchmark maneuvers using a scaled vehicle observed by a motion-capture system. Using Gaussian Process Regression, we develop single-step as well as multi-step prediction models that are usable for reactive as well as predictive model-based control techniques.