One of the key promises of IT strategic outsourcing is to deliver greater IT service management through better quality and lower cost. However, this raises a critical question on how to model highly variable services for diverse customers with heterogeneous infrastructure and service demands. In this paper we propose the use of statistical learning approaches for service operation variability modeling. Specifically, we use the partial least squares regression that projects service attributes to explain the service volume variability, and the decision tree approach to model the service effort based on categorical customer and service properties. We demonstrate the applicability of the proposed methodology using data from a large IT service delivery environment.