The survival function is more steady than the density function because it always exists. Yari et.al (2013) introduced a new entropy based divergence measure which is much similar to Kullback-Leibler divergence named as Kullback-Leibler Divergence for survival function which measures the distance between an empirical and a prescribed survival function and is a much easier to compute in continuous distributions than the Kullback-Leibler divergence. It is worth noted that KLS converges to zero with increasing sample size. In this chapter, we have considered three continuous probability distributions viz., Gompertz distribution, Rayleigh distribution and Exponential-Lomax distribution and obtained the Kullback Leibler Divergence survival function estimates to check the performance of the new estimation method with the commonly used method such as Maximum Likelihood in scale parameter estimation.