Feature selection techniques in machine learning aim to identify and remove unnecessary features from the data. They form part of the larger umbrella of dimensionality reduction processes that aid machine learning (ML) models perform better by enhancing the interpretability of the model, reducing the training time, preventing overfitting, and improving their accuracy.

• Universality: Feature selection techniques can be applied in a cross-section of industries where data analysis is carried out. These include healthcare, finance, robotics, marketing among many others.
• Standard Modules: They comprise a range of standard procedures such as filter methods, wrapper-based methods, and embedded methods, each designed to optimize the performance of the model in different ways.
• Flexible Tailoring: While there are common or standard feature selection methods, the procedures can also be heavily tailored depending on the nature of specific projects.
• Community Supported: Many open-source machine learning libraries like scikit-learn support various feature selection methods, and the ML communities around these libraries often provide support and resources to assist users.
• Efficiency Enhancement: It helps in improving the performance of extensively used machine learning algorithms, making them more versatile and accurate.

Implementing feature selection techniques correctly requires comprehensive understanding of the problem at hand as well as expertise in selecting the correct technique s per the needs of the given machine learning model. A carefully planned approach that involves thorough analysis of the data, thoughtful consideration of the machine learning algorithm(s) in use, and precise identification of unnecessary features can maximize the benefits of feature selection, while carefully avoiding potential pitfalls.

It is advisable making use of the user communities and forums surrounding programming toolkits and machine learning libraries for advice and insights during the feature selection process. Many libraries and toolkits feature built-in methods for feature selection, providing users with a practical starting point in their feature selection tasks.

Opting for feature selection techniques provides several benefits including:

• Training Efficiency: Reducing the number of features reduces the amount of data the model needs to handle, effectively cutting down the time taken to train a model.
• Improved Performance: Feature selection techniques can improve the predictive performance of the models by eliminating irrelevant and redundant features. It can significantly improve the effectiveness of algorithms by reducing overfitting and increasing model interpretability.
• Reduced Complexity: The reduced dimensionality from feature selection techniques simplifies models, making them easier to interpret and discuss.
• Lower Storage Requirements: A model with fewer features requires lesser storage space, which also supports faster computation of the models.
• Enhanced Generalizability: By avoiding overfitting, feature selection techniques improve the model's ability to generalize findings for a larger or different group of data.

Despite several advantages, there are some challenges that users must consider:

• Risk of Loss of Information: When reducing dimensions, there is a risk of losing important information if valuable features are wrongly identified as less important and removed.
• Calibration Sensitivity: Feature selection may result in models that are hyper-tuned to the specific features of the data and lose their ability to predict outcomes when new variables come into play.
• Complexity in Implementation: While the aim is to simplify the model, the actual process of feature selection can be complex and time-consuming. The process might involve various techniques like filter, wrapper, or embedded methods, each with its complexities.
• Increased Computational Load: Some feature selection methods, especially wrapper methods, crave high computational power, making them unsuitable for very high-dimensional datasets.
• Lack of Influence: Much like users of COTS software, users of feature selection techniques exert very little to no influence on the development trajectory of these techniques, which may lead to the future development of the techniques diverging from the user’s specific requirements.

In conclusion, Feature Selection Techniques as a powerful tool in the realm of machine learning offer a slew of benefits including efficiency, improved performance, generalizability, reduced complexity, and lower storage requirements. However, their application comes with challenges. They are often successfully overcome through the application of an iterative process that optimizes the feature selection technique to the specifics of the machine learning model and the data in question. Overall, they contribute significantly to the creation of robust, accurate, and efficient models.