The metric distortion framework asks voting rules to elect a candidate close to the voters using only their rankings. SimultaneousPluralityVeto elects from the plurality veto core and achieves the optimal worst-case distortion of 3, but it is not Pareto-efficient: it can declare a tie between two candidates even when every voter prefers one of them. Removing Pareto-dominated candidates afterward would break the convexity of tied winner sets. We instead introduce regularization, which adds a small $\varepsilon$ to every candidate's weight, restoring Pareto-efficiency. To study its effect on distortion, we introduce the rank score veto core, whose candidate weights come from a positional scoring vector $\mathbf{s}$, and prove that its distortion lies in $[1 + 2(||\mathbf{s}|| - s_m),\, 1 + 2||\mathbf{s}||]$; we conjecture that the lower bound is tight. A fixed $\varepsilon$ can increase distortion, but letting $\varepsilon$ depend on the number of voters preserves the distortion guarantee of the original veto core. Regularized SimultaneousPluralityVeto thus has optimal distortion, is Pareto-efficient, and has convex tied winner sets, resolving an open question of Kızılkaya and Kempe.

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