Cross-Validation Techniques, commonly abbreviated as CV, are statistical methods used for assessing the performance of machine learning and statistical models. The primary goal of these techniques is to estimate how accurately a predictive model will perform in practice, apart from determining the most suitable model. Cross-validation techniques are fundamentally designed to overcome problems like model fitting, parameter tuning and to avoid overfitting or underfitting of a model on a particular dataset.

• Robust Testing: Cross-validation techniques provide an extensive method of assessing the model’s performance on unseen data.
• Bias-Variance Trade-off: Helps in achieving a balance between bias and variance, ensuring the model is neither underfit nor overfit.
• Efficient Utilization of Data: In these techniques, the entire dataset is used for training and validation, maximizing the usage of available data.
• Model Selection: Cross-validation techniques can compare and select the best model, demonstrating accurate results with unseen data.
• Hyperparameter Tuning: These techniques may play a crucial role in the selection of hyperparameters, adjusting the complexity of the model and its performance.

Cross-Validation Techniques are commonly used across multiple industries due to their efficiency in evaluating model performance, increasing reliability while reducing the risk of overfitting.

When deploying cross-validation techniques, it is crucial to understand the dataset and the problem at hand. The correct choice of technique would rely heavily on these factors. Techniques such as K-Fold Cross Validation, Leave One Out Cross Validation (LOOCV), Stratified Cross Validation, and others each have their unique characteristics and are best suited for particular data types and problems.

The implementation should begin with understanding the dataset and selecting the most suitable cross-validation technique. It is followed by applying the technique and evaluating model performance based on statistical measures or validation scores.

Training and testing, though separate processes in model creation, must work closely together to bring about robust, dependable models. Cross-validation techniques are instrumental in achieving this collaboration, thereby making them a valuable tool in the field of machine learning and statistics. Thus, the impact of cross-validation techniques on the validity and reliability of machine learning models is tremendous.

Cross-Validation Techniques come with several inherent advantages, including:

• Comprehensive Assessment: Cross-validation techniques allow for a more robust analysis of the model’s capacity to generalize unseen data. The technique uses all the data for training and testing, thereby providing a more comprehensive insight into model performance.
• Model Comparison: Through validation scores, these techniques enable the ability to compare and select the best model that provides high performance with unseen data.
• Bias-Variance Trade-off: Helps in maintaining a balance between variance and bias, reducing the chances of model overfitting and underfitting.
• Avoid Wastage: By using the complete dataset for training and validation, the techniques maximize data usage, avoiding wastage and providing more reliable performance estimates.

Despite the numerous advantages, Cross-Validation Techniques also come with certain limitations:

• Intensive Computation: As these techniques involve numerous iterations, they tend to be compute-intensive and might take a longer processing time.
• Random Splits: The method of randomly splitting the data can lead to varying results, which might sometimes lead to confusion in the evaluation of model performance.
• Model Selection: Even though these techniques are good for model comparison, the selection of a model is limited only to the sampled data.
• Risk of Overfitting: If the cross-validation process is not carefully monitored, it might lead to model overfitting, especially when tuning hyperparameters.