Small area estimation (SAE) produces estimates of population parameters for geographic and demographic subgroups with limited sample sizes. Such estimates are critical for informing policy decisions, ranging from poverty mapping to social program funding. Despite its widespread use, principled validation of SAE models remains challenging and general guidelines are far from well-established. Unlike conventional predictive modeling settings, validation data are rarely available in the SAE context. External validation surveys or censuses often do not exist, and access to individual-level microdata is often restricted, making standard cross-validation infeasible. In this paper, we propose a novel model validation scheme using only area-level direct survey estimates under the widely used Fay--Herriot model. Our approach is based on data thinning, which splits area-level observations into independent training and test components to enable out-of-sample validation. Our theoretical analysis reveals a fundamental tension inherent in thinning-based validating: performance metrics measured on the thinned training component targets a different quantity than that based on the full data, with the gap varying by model complexity. Increasing the information allocated for training reduces this gap but inflates the variance of the estimator. We formally characterize this bias-variance tradeoff and provide practical recommendations for the thinning parameters that balance these competing considerations for model comparison. We show that data thinning with these settings provides consistent and stable performance across heterogeneous sampling designs in design-based simulations using American Community Survey microdata.

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