There has been much interest in the asymptotic behaviour of radar clutter models, since if the model applied to real data can be approximated by a simpler limiting distribution, then it may be justifiable to apply decision rules constructed for this limiting distribution. One can consider the possibility of applying detectors, designed to operate in exponentially distributed clutter, when the Pareto model is approximately exponentially distributed. Towards this objective, several recent studies have attempted to quantify the validity of the Pareto-exponential approximation. In this chapter, the Kullback-Leibler divergence is used to assess the discrepancy between the Pareto Type II and exponential distributions, in order to better understand the validity of the exponential approximation of the Pareto model. The chapter shows that for any given Pareto model an optimal exponential approximation exists. This approximation is shown to improve as the Pareto shape parameter increased, for any fixed Pareto scale parameter.